Methodologic Publications

  1. Aschard H, Spiegelman D, Laville V, Kraft P, Wang M. A test for gene–environment interaction in the presence of measurement error in the environmental variable. Genet Epidemiol. 2018;1–15.
  2. Wong BHW, Peskoe SB, Spiegelman D. The effect of risk factor misclassification on the partial population attributable risk. Statistics in Medicine. 2018; 1–17. https://doi-org.ezp-prod1.hul.harvard.edu/10.1002/sim.7559.
  3. Spiegelman D, Khudyakov P, Wang M, Vanderweele TJ. Evaluating Public Health Interventions: 7. Let the Subject Matter Choose the Effect Measure: Ratio, Difference, or Something Else Entirely. American Journal of Public Health. 2018; 108(1):73-76.
  4. Thomas EG, Peskoe SB, Spiegelman D. Prevalence estimation when disease status is verified only among test positives: Applications in HIV screening programs. Statistics in Medicine. 2017;1–14. https://doi-org.ezp-prod1.hul.harvard.edu/10.1002/sim.7568
  5. Nevo D, Liao X, Spiegelman D. Estimation and Inference for the Mediation Proportion. The International Journal of Biostatistics. 2017 Sep 20. doi:10.1515/ijb-2017-0006. [Epub ahead of print]
  6. Walsh FJ, Bärnighausen T, Delva W, Fleming Y, Khumalo G, Lejeune CL, Mazibuko S, Mlambo CK, Reis R, Spiegelman D, Zwane M. Impact of early initiation versus national standard of care of antiretroviral therapy in Swaziland’s public sector health system: study protocol for a stepped-wedge randomized trial. Trials. 2017 Aug 18;18(1):38.
  7. Spiegelman D, VanderWeele TJ. Evaluating Public Health Interventions: 6. Modeling Ratios or Differences? Let the Data Tell Us. American Journal of Public Health. 2017 July;107(7):1087-91.
  8. Liao X, Zhou X, Wang M, Hart JE, Laden F, Spiegelman D. Survival analysis with functions of mismeasured covariate histories: the case of chronic air pollution exposure in relation to mortality in the nurses’ health study. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2017 June; doi:10.1111/rssc.12229. [Epub ahead of print].
  9. Zhou X, Liao X, Spiegelman D. “Cross-sectional” stepped wedge designs always reduce the required sample size when there is no time effect. J Clin Epidemiol. 2017 Mar 31;83:108-9.
  10. Glymour MM, Spiegelman D. Evaluating Public Health Interventions: 5. Causal Inference in Public Health Research-Do Sex, Race, and Biological Factors Cause Health Outcomes? Am J Public Health. 2017 Jan; 107(1):81-85.
  11. Barnhart D, Hertzmark E, Liu E, Mungure E, Muya AN, Sando D, Chalamilla G, Ulenga N, Bärnighausen T, Fawzi W, Spiegelman D. Intra-Cluster Correlation Estimates for HIV-related Outcomes from Care and Treatment Clinics in Dar es Salaam, Tanzania. Contemp Clin Trials Commun. 2016 Dec 15; 4:161-169.
  12. Canning D, Shah IH, Pearson E, Pradhan E, Karra M, Senderowicz L, Bärnighausen T, Spiegelman D, Langer A. Institutionalizing postpartum intrauterine device (IUD) services in Sri Lanka, Tanzania, and Nepal: study protocol for a cluster-randomized stepped-wedge trial. BMC Pregnancy Childbirth. 2016 Nov 21; 16(1):362.
  13. Spiegelman D. Spiegelman Responds. Am J Public Health. 2016;106(6):e13-4.
  14. Spiegelman D. Evaluating public health interventions 4: The role of the Nurses’ Health Study in developing and disseminating methods for eliminating bias due to measurement error and misclassification in observational research. Am J Public Health, 2016 Sep; 106 (9):1563-6.
  15. Crippa A, Khudyakov P, Wang M, Orsini N, Spiegelman D. A new measure of between-studies heterogeneity in meta-analysis. Stat Med. 2016;35(21):3661-75.
  16. Spiegelman D. Evaluating Public Health Interventions: 2. Stepping Up to Routine Public Health Evaluation With the Stepped Wedge Design. Am J Public Health. 2016;106(3):453-7.
  17. Spiegelman D, Rivera-Rodriguez CL, Haneuse S. Evaluating Public Health Interventions: 3. The Two-Stage Design for Confounding Bias Reduction-Having Your Cake and Eating It Two. Am J Public Health. 2016;106(7):1223-6.
  18. Wang M, Liao X, Laden F, Spiegelman D. Quantifying risk over the life course – latency, age-related susceptibility, and other time-varying exposure metrics. Stat Med. 2016; 35(13): 2283-95.
  19. Wang M, Spiegelman D, Kuchiba A, Lochhead P, Kim S, Chan AT, Poole EM, Tamimi R, Tworoger SS, Giovannucci E, Rosner B, Ogino S. Statistical methods for studying disease subtype heterogeneity. Stat Med. 2016;35(5):782-800.
  20. Spiegelman D. Evaluating Public Health Interventions: 1. Examples, Definitions, and a Personal Note. Am J Public Health. 2016;106(1):70-3.
  21. Liao X, Zhou X, Spiegelman D. A note on “Design and analysis of stepped wedge cluster randomized trials”. Contemporary clinical trials. 2015;45(Pt B):338-9.
  22. Khudyakov P, Gorfine M, Zucker D, Spiegelman D. The impact of covariate measurement error on risk prediction. Stat Med. 2015; 34(15):2353-67.
  23. Kim S, Li Y, Spiegelman D. A semiparametric copula method for Cox models with covariate measurement error. Lifetime Data Anal. 2014.
  24. Yi GY, Ma Y, Spiegelman D, Carroll RJ. Functional and Structural Methods with Mixed Measurement Error and Misclassification in Covariates. Journal of the American Statistical Association. 2014:00-00.
  25. Kioumourtzoglou MA, Spiegelman D, Szpiro AA, et al. Exposure measurement error in PM2.5 health effects studies: a pooled analysis of eight personal exposure validation studies. Environ. Health. 2014;13(1):2.
  26. Zucker DM, Gorfine M, Li Y, Tadesse MG, Spiegelman D. A regularization corrected score method for nonlinear regression models with covariate error. Biometrics. 2013;69(1):80-90.
  27. Zucker DM, Agami S, Spiegelman D. Testing for a Changepoint in the Cox Survival Regression Model. Journal of Statistical Theory and Practice. 2013;7(2):360-380.
  28. Wang M, Liao X, Spiegelman D. Can efficiency be gained by correcting for misclassification? J Stat Plan Inference. 2013;143(11).
  29. Takkouche B, Khudyakov P, Costa-Bouzas J, Spiegelman D. Confidence intervals for heterogeneity measures in meta-analysis. Am. J. Epidemiol. 2013;178(6):993-1004.
  30. Spiegelman D. Regression calibration in air pollution epidemiology with exposure estimated by spatio-temporal modeling. Environmetrics. 2013;24(8):521-524.
  31. Liao X, Spiegelman D, Carroll RJ. Regression calibration is valid when properly applied. Epidemiology. 2013;24(3):466-467.
  32. Kim S, Zeng D, Li Y, Spiegelman D. Joint Modeling of Longitudinal and Cure-survival Data. J Stat Theory Pract. 2013;7(2):324-344.
  33. Barrera-Gomez J, Spiegelman D, Basagana X. Optimal combination of number of participants and number of repeated measurements in longitudinal studies with time-varying exposure. Stat. Med. 2013;32(27):4748-4762.
  34. Orsini N, Li R, Wolk A, Khudyakov P, Spiegelman D. Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. Am. J. Epidemiol. 2012;175(1):66-73.
  35. Spiegelman D, Logan R, Grove D. Regression calibration with heteroscedastic error variance. Int J Biostat. 2011;7(1):4.
  36. Preis SR, Spiegelman D, Zhao BB, Moshfegh A, Baer DJ, Willett WC. Application of a repeat-measure biomarker measurement error model to 2 validation studies: examination of the effect of within-person variation in biomarker measurements. Am. J. Epidemiol. 2011;173(6):683-694.
  37. Liao X, Zucker DM, Li Y, Spiegelman D. Survival analysis with error-prone time-varying covariates: a risk set calibration approach. Biometrics. 2011;67(1):50-58.
  38. Basagana X, Liao X, Spiegelman D. Power and sample size calculations for longitudinal studies estimating a main effect of a time-varying exposure. Stat. Methods Med. Res. 2011;20(5):471-487.
  39. Spiegelman D. Commentary: some remarks on the seminal 1904 paper of Charles Spearman ‘The proof and measurement of association between two things’. Int. J. Epidemiol. 2010;39(5):1156-1159.
  40. Spiegelman D. Approaches to uncertainty in exposure assessment in environmental epidemiology. Annu. Rev. Public Health. 2010;31:149-163.
  41. Basagana X, Spiegelman D. Power and sample size calculations for longitudinal studies comparing rates of change with a time-varying exposure. Stat. Med. 2010;29(2):181-192.
  42. Malloy EJ, Spiegelman D, Eisen EA. Comparing measures of model selection for penalized splines in Cox models. Comput Stat Data Anal. 2009;53(7):2605-2616.
  43. Lindstrom S, Yen YC, Spiegelman D, Kraft P. The impact of gene-environment dependence and misclassification in genetic association studies incorporating gene-environment interactions. Hum. Hered. 2009;68(3):171-181.
  44. Govindarajulu US, Malloy EJ, Ganguli B, Spiegelman D, Eisen EA. The comparison of alternative smoothing methods for fitting non-linear exposure-response relationships with Cox models in a simulation study. Int J Biostat. 2009;5(1):Article 2.
  45. Zucker DM, Spiegelman D. Corrected score estimation in the proportional hazards model with misclassified discrete covariates. Stat. Med. 2008;27(11):1911-1933.
  46. Van Roosbroeck S, Li R, Hoek G, Lebret E, Brunekreef B, Spiegelman D. Traffic-related outdoor air pollution and respiratory symptoms in children: the impact of adjustment for exposure measurement error. Epidemiology. 2008;19(3):409-416.
  47. Spiegelman D. Commentary: Calculations of EPIC proportions. Int. J. Epidemiol. 2008;37(2):379-381.
  48. Ritz J, Demidenko E, Spiegelman D. Multivariate meta-analysis for data consortia, individual patient meta-analysis, and pooling projects. Journal of Statistical Planning and Inference. 2008;138(7):1919-1933.
  49. Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference. 2007;137(2):449-461.
  50. Spiegelman D, Hertzmark E, Wand HC. Point and interval estimates of partial population attributable risks in cohort studies: examples and software. Cancer Causes Control. 2007;18(5):571-579.
  51. Govindarajulu US, Spiegelman D, Thurston SW, Ganguli B, Eisen EA. Comparing smoothing techniques in Cox models for exposure-response relationships. Stat. Med. 2007;26(20):3735-3752.
  52. Spiegelman D, Hertzmark E. The authors reply to Neogi & Zhang, Tian & Liu, and Petersen & Deddens re: ‘Easy SAS calculations for risk or prevalence ratios and differences’. Am. J. Epidemiol. 2006;163(12):1159-1161.
  53. Spiegelman D. Relative Risk. Encyclopedia of Environmetrics: John Wiley & Sons, Ltd; 2006.
  54. Spiegelman D. Validation Studies. Encyclopedia of Environmetrics: John Wiley & Sons, Ltd; 2006.
  55. Li R, Weller E, Dockery DW, Neas LM, Spiegelman D. Association of indoor nitrogen dioxide with respiratory symptoms in children: application of measurement error correction techniques to utilize data from multiple surrogates. J. Expo. Sci. Environ. Epidemiol. 2006;16(4):342-350.
  56. Horick N, Weller E, Milton DK, Gold DR, Li R, Spiegelman D. Home endotoxin exposure and wheeze in infants: correction for bias due to exposure measurement error. Environ. Health Perspect. 2006;114(1):135-140.
  57. Thurston SW, Williams PL, Hauser R, Hu H, Hernandez-Avila M, Spiegelman D. A comparison of regression calibration approaches for designs with internal validation data. Journal of Statistical Planning and Inference. 2005;131(1):175-190.
  58. Spiegelman D, Zhao B, Kim J. Correlated errors in biased surrogates: study designs and methods for measurement error correction. Stat. Med. 2005;24(11):1657-1682.
  59. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or prevalence ratios and differences. Am. J. Epidemiol. 2005;162(3):199-200.
  60. Spiegelman D. Validation Study. Encyclopedia of Biostatistics: John Wiley & Sons, Ltd; 2005.
  61. Spiegelman D. Reliability Study. Encyclopedia of Biostatistics: John Wiley & Sons, Ltd; 2005.
  62. Zucker DM, Spiegelman D. Inference for the proportional hazards model with misclassified discrete-valued covariates. Biometrics. 2004;60(2):324-334.
  63. Spiegelman D. Commentary: Correlated errors and energy adjustment–where are the data? Int. J. Epidemiol. 2004;33(6):1387-1388.
  64. Ritz J, Spiegelman D. Equivalence of conditional and marginal regression models for clustered and longitudinal data. Stat. Methods Med. Res. 2004;13(4):309-323.
  65. Bang H, Spiegelman D. Estimating treatment effects in studies of perinatal transmission of HIV. Biostatistics. 2004;5(1):31-43.
  66. Thurston SW, Spiegelman D, Ruppert D. Equivalence of regression calibration methods in main study/external validation study designs. Journal of Statistical Planning and Inference. 2003;113(2):527-539.
  67. Keshaviah AP, Weller E, Spiegelman D. Occupational exposure to methyl tertiary butyl ether in relation to key health symptom prevalence: the effect of measurement error correction. Environmetrics. 2003;14(6):573-582.
  68. Sturmer T, Thurigen D, Spiegelman D, Blettner M, Brenner H. The performance of methods for correcting measurement error in case-control studies. Epidemiology. 2002;13(5):507-516.
  69. Staudenmayer J, Spiegelman D. Segmented regression in the presence of covariate measurement error in main study/validation study designs. Biometrics. 2002;58(4):871-877.
  70. Spiegelman D, Carroll RJ, Kipnis V. Efficient regression calibration for logistic regression in main study/internal validation study designs with an imperfect reference instrument. Stat. Med. 2001;20(1):139-160.
  71. Kabagambe EK, Baylin A, Allan DA, Siles X, Spiegelman D, Campos H. Application of the Method of Triads to Evaluate the Performance of Food Frequency Questionnaires and Biomarkers as Indicators of Long-term Dietary Intake. Am. J. Epidemiol. 2001;154(12):1126-1135.
  72. Costa-Bouzas J, Takkouche B, Cadarso-Suarez C, Spiegelman D. HEpiMA: software for the identification of heterogeneity in meta-analysis. Comput. Methods Programs Biomed. 2001;64(2):101-107.
  73. Thurigen D, Spiegelman D, Blettner M, Heuer C, Brenner H. Measurement error correction using validation data: a review of methods and their applicability in case-control studies. Stat. Methods Med. Res. 2000;9(5):447-474.
  74. Spiegelman D, Rosner B, Logan R. Estimation and Inference for Logistic Regression with Covariate Misclassification and Measurement Error in Main Study/Validation Study Designs. Journal of the American Statistical Association. 2000;95(449):51-61.
  75. Takkouche B, Cadarso-Suarez C, Spiegelman D. Evaluation of old and new tests of heterogeneity in epidemiologic meta-analysis. Am. J. Epidemiol. 1999;150(2):206-215.
  76. Morrissey MJ, Spiegelman D. Matrix methods for estimating odds ratios with misclassified exposure data: extensions and comparisons. Biometrics. 1999;55(2):338-344.
  77. Holcroft CA, Spiegelman D. Design of validation studies for estimating the odds ratio of exposure-disease relationships when exposure is misclassified. Biometrics. 1999;55(4):1193-1201.
  78. Spiegelman D, Valanis B. Correcting for bias in relative risk estimates due to exposure measurement error: a case study of occupational exposure to antineoplastics in pharmacists. Am. J. Public Health. 1998;88(3):406-412.
  79. Spiegelman D, Schneeweiss S, McDermott A. Measurement error correction for logistic regression models with an “alloyed gold standard”. Am. J. Epidemiol. 1997;145(2):184-196.
  80. Spiegelman D, McDermott A, Rosner B. Regression calibration method for correcting measurement-error bias in nutritional epidemiology. Am. J. Clin. Nutr. 1997;65(4 Suppl):1179s-1186s.
  81. Spiegelman D, Casella M. Fully parametric and semi-parametric regression models for common events with covariate measurement error in main study/validation study designs. Biometrics. 1997;53(2):395-409.
  82. Foppa I, Spiegelman D. Power and sample size calculations for case-control studies of gene-environment interactions with a polytomous exposure variable. Am. J. Epidemiol. 1997;146(7):596-604.
  83. Dernidenko E, Spiegelman D. A paradox: more measurement error can lead to more efficient estimates. Communications in Statistics – Theory and Methods. 1997;26(7):1649-1675.
  84. Spiegelman D, Hunter D, Hertzmark E, Colditz G, Gail M, Benichou J. Responses to Atkins J. Natl. Cancer Inst. 1994;86(17):1350-1352.
  85. Spiegelman D, Colditz GA, Hunter D, Hertzmark E. Validation of the Gail et al. Model for Predicting Individual Breast Cancer Risk. J. Natl. Cancer Inst. 1994;86(8):600-607.
  86. Spiegelman D. Statistical methods in clinical trials analysis. In: Cramer DW, Goldman MB, eds. Infertility and reproductive medicine: study designs and statistics April 1994 (Clinics of North America, Volume 5 No. 2). Philadelphia, PA: WB Saunders; 1994:365-385.
  87. Spiegelman D. Cost-efficient study designs for relative risk modeling with covariate measurement error. Journal of Statistical Planning and Inference. 1994;42(1–2):187-208.
  88. Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for random within-person measurement error. Am. J. Epidemiol. 1992;136(11):1400-1413.
  89. Spiegelman D, Gray R. Cost-efficient study designs for binary response data with Gaussian covariate measurement error. Biometrics. 1991;47(3):851-869.
  90. Spiegelman D, Bailar J, III, Crouch EC, Shaikh R. Is the One-Hit Model of Carcinogenesis Conservative? Evidence from the National Toxicology Program’s Bioassay Databasea. In: Cox L, Jr., Ricci P, eds. New Risks: Issues and Management. Vol 6: Springer US; 1990:65-76.
  91. Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. Am. J. Epidemiol. 1990;132(4):734-745.
  92. Crouch EAC, Spiegelman D. The Evaluation of Integrals of the form ∫+∞ −∞ f(t)exp(−t 2) dt: Application to Logistic-Normal Models. Journal of the American Statistical Association. 1990;85(410):464-469.
  93. Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Stat. Med. 1989;8(9):1051-1069; discussion 1071-1053.
  94. Bailar JC, 3rd, Crouch EA, Shaikh R, Spiegelman D. One-hit models of carcinogenesis: conservative or not? Risk Anal. 1988;8(4):485-497.
  95. Spiegelman D, Wang JD, Wegman D. Interactive electronic computing of the mortality odds ratio. Am. J. Epidemiol. 1983;118(4):599-607.